Evidence map›Paper›PMID 41695046›Full record

ArticleJPGN reports2026

Can artificial intelligence pass the test? Evaluating chatbot scores on pediatric gastroenterology board-style questions.

Annette G Roberts, Reshma Patel, Sharmilaa Babu, Matthew M Engelhard, Rachel G Greenberg, Arun Ajmera

Abstract read
In one paragraph

Article in JPGN reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Annette G RobertsDivision of Gastroenterology, Hepatology, and Nutrition Duke University Hospital Durham North Carolina USA.ORCID https://orcid.org/0000-0003-2475-0167
Reshma PatelDivision of Gastroenterology, Hepatology, and Nutrition Duke University Hospital Durham North Carolina USA.
Sharmilaa BabuDivision of Gastroenterology, Hepatology, and Nutrition Duke University Hospital Durham North Carolina USA.
Matthew M EngelhardDepartment of Biostatistics and Bioinformatics Duke University School of Medicine Durham North Carolina USA.
Rachel G GreenbergDuke Clinical Research Institute Durham North Carolina USA.
Arun AjmeraDivision of Gastroenterology, Hepatology, and Nutrition Duke University Hospital Durham North Carolina USA.ORCID https://orcid.org/0009-0007-2640-8070

Funding

Machine Learning Methods to Develop and Deploy Real-Time Risk Surveillance for Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder from the Electronic Health RecordK01MH127309 · NIMH · DUKE UNIVERSITY · PI Matthew Morrow Engelhard · 2022 to 2026
$842k
NIMH NIH HHS K01 MH127309
6 · The paper itself

Abstract

Objectives: The American Academy of Pediatrics (AAP) Pediatrics Review and Education Program (PREP)® Gastroenterology (GI) Self-Assessments help pediatric gastroenterologists and trainees prepare for subspecialty board exams by providing peer-reviewed questions and critiques based on American Board of Pediatrics content specifications. These assessments test knowledge of material aligned with the pediatric gastroenterology board exams. While artificial intelligence (AI) chatbots have passed various medical board exams, their ability to pass the pediatric GI boards remains untested. This study assesses the performance of Microsoft Copilot and OpenAI ChatGPT-3.5 and 4o on the 2022-2024 AAP PREP® GI Self-Assessments. Methods: A total of 216 AAP PREP® GI Self-Assessment questions from 2022 to 2024 were entered into three AI chatbots (Microsoft Copilot, OpenAI ChatGPT-3.5, and ChatGPT-4o). Scores were compared with the passing score (> 65%) and first-time test takers' scores from the AAP for 2022-2024. Results: OpenAI ChatGPT-4o and Microsoft Copilot scored above 65% (pass) on all three PREP® GI Self-Assessments from 2022 to 2024. OpenAI ChatGPT-3.5 passed the 2023 and 2024 assessments but did not pass the 2022 assessment. The chatbots collectively scored best in anatomy, motility, and mouth and esophageal disorders, and scored poorly in physiology, pharmacology, liver, stomach and duodenum disorders. Conclusions: OpenAI ChatGPT-4o and Microsoft Copilot consistently passed the PREP® GI Self-Assessments from 2022 to 2024, showing potential for good performance on the pediatric GI boards. OpenAI ChatGPT-3.5 had limitations, passing only the 2023 and 2024 assessments. Overall, advanced AI chatbots show potential to pass the Pediatric GI board exam.

Indexed as

ChatGPT‐4oMicrosoft CopilotOpenAI ChatGPT‐3.5pediatric gastroenterology board exampediatric specialty exam

Identifiers

PMID41695046
PMCPMC12894084

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.